#108 Vanderbilt (4-8)

avg: 1327.62  •  sd: 91.98  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
43 Alabama-Huntsville Win 13-10 2031.28 Jan 28th T Town Throwdown1
88 Central Florida Win 13-10 1762.36 Jan 28th T Town Throwdown1
131 Georgia State Win 12-11 1367.58 Jan 28th T Town Throwdown1
43 Alabama-Huntsville Loss 8-11 1337.52 Jan 29th T Town Throwdown1
61 Emory Loss 11-13 1348.14 Jan 29th T Town Throwdown1
59 Cincinnati Loss 1-13 978.71 Apr 1st Huck Finn1
61 Emory Loss 5-6 1451.98 Apr 1st Huck Finn1
49 Notre Dame Loss 6-11 1096.56 Apr 1st Huck Finn1
22 Washington University Loss 3-8 1305.32 Apr 1st Huck Finn1
90 Chicago Loss 10-11 1308.78 Apr 2nd Huck Finn1
92 Missouri S&T Loss 5-13 829.82 Apr 2nd Huck Finn1
131 Georgia State Win 10-7 1632.25 Apr 2nd Huck Finn1
**Blowout Eligible


The uncertainty of the mean is equal to the standard deviation of the set of game ratings, divided by the square root of the number of games. We treated a team’s ranking as a normally distributed random variable, with the USAU ranking as the mean and the uncertainty of the ranking as the standard deviation
  1. Calculate uncertainy for USAU ranking averge
  2. Model ranking as a normal distribution around USAU averge with standard deviation equal to uncertainty
  3. Simulate seasons by drawing a rank for each team from their distribution. Note the teams in the top 16 (club) or top 20 (college)
  4. Sum the fractions for each region for how often each of it's teams appeared in the top 16 (club) or top 20 (college)
  5. Subtract one from each fraction for "autobids"
  6. Award remainings bids to the regions with the highest remaining fraction, subtracting one from the fraction each time a bid is awarded
There is an article on Ulitworld written by Scott Dunham and I that gives a little more context (though it probably was the thing that linked you here)